diff --git a/mindspore/ccsrc/backend/kernel_compiler/cpu/assign_cpu_kernel.cc b/mindspore/ccsrc/backend/kernel_compiler/cpu/assign_cpu_kernel.cc index a1c4cd115bf..4064190e192 100644 --- a/mindspore/ccsrc/backend/kernel_compiler/cpu/assign_cpu_kernel.cc +++ b/mindspore/ccsrc/backend/kernel_compiler/cpu/assign_cpu_kernel.cc @@ -53,9 +53,9 @@ bool AssignCPUKernel::Launch(const std::vector &inputs, const std::v MS_LOG(EXCEPTION) << "Memcpy size must <= max_size, but got memcpy size is : " << total_size << ", max size is : " << max_size; } - int ret = memcpy_s(inputs[0]->addr, total_size, inputs[1]->addr, total_size); + int ret = memcpy_s(inputs[0]->addr, max_size, inputs[1]->addr, total_size); if (ret != 0) { - MS_LOG(EXCEPTION) << "memcpy_s error, errorno" << ret; + MS_LOG(EXCEPTION) << "memcpy_s error, error no " << ret; } return true; } diff --git a/mindspore/ccsrc/backend/kernel_compiler/cpu/maximum_cpu_kernel.cc b/mindspore/ccsrc/backend/kernel_compiler/cpu/maximum_cpu_kernel.cc index 5fa83d8487c..8e4ffd5dcd5 100644 --- a/mindspore/ccsrc/backend/kernel_compiler/cpu/maximum_cpu_kernel.cc +++ b/mindspore/ccsrc/backend/kernel_compiler/cpu/maximum_cpu_kernel.cc @@ -19,7 +19,6 @@ namespace mindspore { namespace kernel { - template void MaximumCPUKernel::InitKernel(const CNodePtr &kernel_node) { CheckParam(kernel_node); @@ -216,6 +215,5 @@ void MaximumCPUKernel::BroadcastArithTensors(const T *input_x, const T *input output[i] = MaximumFunc(input_x[i], input_y[i]); } } - } // namespace kernel } // namespace mindspore diff --git a/mindspore/nn/layer/__init__.py b/mindspore/nn/layer/__init__.py index 4aecf85d976..3c4a601c1cc 100644 --- a/mindspore/nn/layer/__init__.py +++ b/mindspore/nn/layer/__init__.py @@ -17,7 +17,8 @@ Layer. The high-level components(Cells) used to construct the neural network. """ -from . import activation, normalization, container, conv, lstm, basic, embedding, pooling, image, quant, math, combined +from . import activation, normalization, container, conv, lstm, basic, embedding, pooling, image, quant, math, \ + combined, timedistributed from .activation import * from .normalization import * from .container import * @@ -30,6 +31,7 @@ from .image import * from .quant import * from .math import * from .combined import * +from .timedistributed import * __all__ = [] __all__.extend(activation.__all__) @@ -44,3 +46,4 @@ __all__.extend(image.__all__) __all__.extend(quant.__all__) __all__.extend(math.__all__) __all__.extend(combined.__all__) +__all__.extend(timedistributed.__all__) diff --git a/mindspore/nn/layer/timedistributed.py b/mindspore/nn/layer/timedistributed.py new file mode 100644 index 00000000000..e7ee93b05bf --- /dev/null +++ b/mindspore/nn/layer/timedistributed.py @@ -0,0 +1,138 @@ +# Copyright 2020 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +"""Time Distributed.""" + +from mindspore.ops.primitive import constexpr, Primitive +from mindspore.ops import Reshape, Transpose, Pack, Unpack +from mindspore.common.dtype import tensor +from ..cell import Cell + +__all__ = ['TimeDistributed'] + + +@constexpr +def _check_reshape_pos(reshape_pos, inputs_shape, outputs_shape): + if reshape_pos >= len(outputs_shape) or inputs_shape[reshape_pos] != outputs_shape[reshape_pos]: + raise ValueError("The parameter reshape_with_axis is invalid in the input and output of TimeDistributed. " + "You may try pass parameters without reshape_with_axis.") + + +@constexpr +def _check_expand_dims_axis(time_axis, ndim): + if time_axis > ndim: + raise ValueError("The parameter time_axis is invalid in the input. " + "The value of time_axis should be in range of [{}, {}].".format(-ndim - 1, ndim)) + + +@constexpr +def _generate_perm(axis_a, axis_b, length): + perm = tuple(range(length)) + axis_a, axis_b = (axis_a, axis_b) if axis_a < axis_b else (axis_b, axis_a) + return perm[:axis_a] + perm[axis_a + 1: axis_b + 1] + (perm[axis_a],) + perm[axis_b + 1:] + + +@constexpr +def _check_data(flag): + if not flag: + raise TypeError("The inputs and outputs shuould be a Tensor.") + + +@constexpr +def _check_inputs_dim(shape): + if len(shape) < 3: + raise ValueError("The inputs should be at least 3D.") + + +class TimeDistributed(Cell): + r""" + The time distributed layer. + + Time distributed is a wrapper which allows to apply a layer to every temporal slice of an input. + And the input should be at least 3D. + There are two cases in the implementation. + When reshape_with_axis provided, the reshape method will be chosen, which is more efficient; + otherwise, the method of dividing the inputs along time axis will be used, which is more general. + For example, reshape_with_axis could not be provided when deal with batch normal. + + Args: + layer(Union[Cell, Primitive]): The Cell or Primitive which will be wrapped. + time_axis(int): The axis of time_step. + reshape_with_axis(int): The axis which time_axis will be reshaped with. Default: 'None'. + + Raises: + TypeError: If cell is not a Cell or Primitive. + + inputs: + -**input**(Tensor)-Tensor of shape: math:'(N, T, *)' + + Outputs: + Tensor of shape: math:'(N, T, *)' + + Supported Platforms: + ``Ascend`` ``GPU`` ``CPU`` + + Examples: + >>> input = Tensor(np.random.random([32, 10, 3]), mindspore.float32) + >>> dense = nn.Dense(3, 6) + >>> net = TimeDistributed(dense, time_axis=1, reshape_with_axis=0) + >>> output = net(input) + >>> print(output.shape) + (32, 10, 6) + """ + + def __init__(self, layer, time_axis, reshape_with_axis=None): + if not isinstance(layer, (Cell, Primitive)): + raise TypeError("Please initialize TimeDistributed with mindspore.nn.Cell or " + "mindspore.ops.Primitive instance. You passed: {input}".format(input=layer)) + super(TimeDistributed, self).__init__() + self.layer = layer + self.time_axis = time_axis + self.reshape_with_axis = reshape_with_axis + self.transpose = Transpose() + self.reshape = Reshape() + + def construct(self, inputs): + _check_data(isinstance(inputs, tensor)) + _check_inputs_dim(inputs.shape) + time_axis = self.time_axis % len(inputs.shape) + if self.reshape_with_axis is not None: + reshape_with_axis = self.reshape_with_axis % len(inputs.shape) + inputs_shape = inputs.shape + time_axis_new = len(inputs_shape) - 2 if reshape_with_axis == len(inputs_shape) - 1 \ + else (reshape_with_axis + 1 if time_axis > reshape_with_axis else + reshape_with_axis - 1) + reshape_pos = time_axis_new if time_axis_new < reshape_with_axis else reshape_with_axis + perm = _generate_perm(time_axis_new, time_axis, len(inputs_shape)) + inputs = self.transpose(inputs, perm) + inputs_shape_new = inputs.shape + inputs = self.reshape(inputs, inputs_shape_new[: reshape_pos] + (-1,) + inputs_shape_new[reshape_pos + 2:]) + outputs = self.layer(inputs) + _check_data(isinstance(outputs, tensor)) + _check_reshape_pos(reshape_pos, inputs.shape, outputs.shape) + outputs_shape_new = outputs.shape[:reshape_pos] + inputs_shape_new[reshape_pos: reshape_pos + 2] + if reshape_pos + 1 < len(outputs.shape): + outputs_shape_new += outputs.shape[reshape_pos + 1:] + return self.reshape(outputs, outputs_shape_new) + + unpack = Unpack(time_axis) + inputs = unpack(inputs) + y = () + for item in inputs: + outputs = self.layer(item) + _check_data(isinstance(outputs, tensor)) + _check_expand_dims_axis(time_axis, outputs.ndim) + y += (outputs,) + y = Pack(time_axis)(y) + return y diff --git a/tests/st/ops/cpu/test_time_distributed_op.py b/tests/st/ops/cpu/test_time_distributed_op.py new file mode 100644 index 00000000000..f6d0a3641b8 --- /dev/null +++ b/tests/st/ops/cpu/test_time_distributed_op.py @@ -0,0 +1,198 @@ +# Copyright 2020 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +import numpy as np +import pytest + +import mindspore +import mindspore.context as context +import mindspore.nn as nn +import mindspore.ops as ops +from mindspore import Tensor + +context.set_context(mode=context.GRAPH_MODE, device_target='CPU') + + +class TestTimeDistributed(nn.Cell): + def __init__(self, cell, time_axis, reshape_with_axis=None): + super(TestTimeDistributed, self).__init__() + self.time_distributed = nn.TimeDistributed(cell, time_axis, reshape_with_axis) + + def construct(self, inputs): + return self.time_distributed(inputs) + + +@pytest.mark.level0 +@pytest.mark.platform_x86_cpu +@pytest.mark.env_onecard +def test_time_distributed_conv2d(): + inputs = np.random.randint(0, 10, [32, 12, 10, 10]) + conv2d = nn.Conv2d(12, 24, 4, has_bias=False, weight_init='normal') + output_expect = conv2d(Tensor(inputs, mindspore.float32)).asnumpy() + inputs = inputs.reshape([32, 1, 12, 10, 10]).repeat(6, axis=1) + time_distributed = TestTimeDistributed(conv2d, time_axis=1, reshape_with_axis=0) + output = time_distributed(Tensor(inputs, mindspore.float32)).asnumpy() + for i in range(output.shape[1]): + assert np.all(np.abs(output[:, i, :] - output_expect) < 1e-5) + print("Conv2D layer wrapped successful") + + +@pytest.mark.level0 +@pytest.mark.platform_x86_cpu +@pytest.mark.env_onecard +def test_time_distributed_maxpool2d(): + inputs = np.random.randint(0, 10, [32, 12, 10, 10]) + pool = nn.MaxPool2d(kernel_size=3, stride=1) + output_expect = pool(Tensor(inputs, mindspore.float32)).asnumpy() + inputs = inputs.reshape([32, 1, 12, 10, 10]).repeat(6, axis=1) + time_distributed = TestTimeDistributed(pool, time_axis=1, reshape_with_axis=0) + output = time_distributed(Tensor(inputs, mindspore.float32)).asnumpy() + for i in range(output.shape[1]): + assert np.all(output[:, i, :] == output_expect) + print("MaxPooling2D layer wrapped successful") + + +@pytest.mark.level0 +@pytest.mark.platform_x86_cpu +@pytest.mark.env_onecard +def test_time_distributed_dense(): + inputs = np.random.randint(0, 10, [32, 10]) + dense = nn.Dense(10, 6) + output_expect = dense(Tensor(inputs, mindspore.float32)).asnumpy() + inputs = inputs.reshape([32, 1, 10]).repeat(6, axis=1) + time_distributed = TestTimeDistributed(dense, time_axis=1, reshape_with_axis=0) + output = time_distributed(Tensor(inputs, mindspore.float32)).asnumpy() + for i in range(output.shape[1]): + assert np.all(output[:, i, :] == output_expect) + print("Dense layer wrapped successful") + + +@pytest.mark.level0 +@pytest.mark.platform_x86_cpu +@pytest.mark.env_onecard +def test_time_distributed_dense_with_reshape_axis_not_first(): + inputs = np.random.randint(0, 10, [32, 10]) + dense = nn.Dense(10, 6) + output_expect = dense(Tensor(inputs, mindspore.float32)).asnumpy() + inputs = inputs.reshape([1, 32, 10]).repeat(6, axis=0) + time_distributed = TestTimeDistributed(dense, time_axis=0, reshape_with_axis=1) + output = time_distributed(Tensor(inputs, mindspore.float32)).asnumpy() + for i in range(output.shape[0]): + assert np.all(output[i, :] == output_expect) + print("Dense layer wrapped successful") + + +@pytest.mark.level0 +@pytest.mark.platform_x86_cpu +@pytest.mark.env_onecard +def test_time_distributed_argmax(): + inputs = np.random.randint(0, 10, [3, 4]) + argmax = ops.Argmax(output_type=mindspore.int32, axis=1) + output_expect = argmax(Tensor(inputs, mindspore.float32)).asnumpy() + inputs = inputs.reshape([3, 1, 4]).repeat(6, axis=1) + time_distributed = TestTimeDistributed(argmax, time_axis=1, reshape_with_axis=0) + output = time_distributed(Tensor(inputs, mindspore.float32)).asnumpy() + for i in range(output.shape[1]): + assert np.all(output[:, i] == output_expect) + print("Argmax op wrapped successful") + + +@pytest.mark.level0 +@pytest.mark.platform_x86_cpu +@pytest.mark.env_onecard +def test_time_distributed_flatten(): + inputs = np.random.randint(0, 10, [3, 4, 5]) + flatten = nn.Flatten() + output_expect = flatten(Tensor(inputs, mindspore.float32)).asnumpy() + inputs = inputs.reshape([3, 1, 4, 5]).repeat(6, axis=1) + time_distributed = TestTimeDistributed(flatten, time_axis=1, reshape_with_axis=0) + output = time_distributed(Tensor(inputs, mindspore.float32)).asnumpy() + for i in range(output.shape[1]): + assert np.all(output[:, i, :] == output_expect) + print("Flatten op wrapped successful") + + +@pytest.mark.level0 +@pytest.mark.platform_x86_cpu +@pytest.mark.env_onecard +def test_time_distributed_conv2d_no_reshape_axis(): + inputs = np.random.randint(0, 10, [32, 12, 10, 10]) + conv2d = nn.Conv2d(12, 24, 4, has_bias=False, weight_init='normal') + output_expect = conv2d(Tensor(inputs, mindspore.float32)).asnumpy() + inputs = inputs.reshape([32, 1, 12, 10, 10]).repeat(6, axis=1) + time_distributed = TestTimeDistributed(conv2d, time_axis=1) + output = time_distributed(Tensor(inputs, mindspore.float32)).asnumpy() + for i in range(output.shape[1]): + assert np.all(output[:, i, :] == output_expect) + print("Conv2D layer with no reshape axis wrapped successful") + + +@pytest.mark.level0 +@pytest.mark.platform_x86_cpu +@pytest.mark.env_onecard +def test_time_distributed_maxpool2d_no_reshape_axis(): + inputs = np.random.randint(0, 10, [32, 12, 10, 10]) + pool = nn.MaxPool2d(kernel_size=3, stride=1) + output_expect = pool(Tensor(inputs, mindspore.float32)).asnumpy() + inputs = inputs.reshape([32, 1, 12, 10, 10]).repeat(6, axis=1) + time_distributed = TestTimeDistributed(pool, time_axis=1) + output = time_distributed(Tensor(inputs, mindspore.float32)).asnumpy() + for i in range(output.shape[1]): + assert np.all(output[:, i, :] == output_expect) + print("MaxPooling2D layer with no reshape axis wrapped successful") + + +@pytest.mark.level0 +@pytest.mark.platform_x86_cpu +@pytest.mark.env_onecard +def test_time_distributed_dense_no_reshape_axis(): + inputs = np.random.randint(0, 10, [32, 10]) + dense = nn.Dense(10, 6) + output_expect = dense(Tensor(inputs, mindspore.float32)).asnumpy() + inputs = inputs.reshape([32, 1, 10]).repeat(6, axis=1) + time_distributed = TestTimeDistributed(dense, time_axis=1) + output = time_distributed(Tensor(inputs, mindspore.float32)).asnumpy() + for i in range(output.shape[1]): + assert np.all(output[:, i, :] == output_expect) + print("Dense layer with no reshape axis wrapped successful") + + +@pytest.mark.level0 +@pytest.mark.platform_x86_cpu +@pytest.mark.env_onecard +def test_time_distributed_argmax_no_reshape_axis(): + inputs = np.random.randint(0, 10, [3, 4]) + argmax = ops.Argmax(output_type=mindspore.int32, axis=1) + output_expect = argmax(Tensor(inputs, mindspore.float32)).asnumpy() + inputs = inputs.reshape([3, 1, 4]).repeat(6, axis=1) + time_distributed = TestTimeDistributed(argmax, time_axis=1) + output = time_distributed(Tensor(inputs, mindspore.float32)).asnumpy() + for i in range(output.shape[1]): + assert np.all(output[:, i] == output_expect) + print("Argmax op with no reshape axis wrapped successful") + + +@pytest.mark.level0 +@pytest.mark.platform_x86_cpu +@pytest.mark.env_onecard +def test_time_distributed_flatten_no_reshape_axis(): + inputs = np.random.randint(0, 10, [3, 4, 5]) + flatten = nn.Flatten() + output_expect = flatten(Tensor(inputs, mindspore.float32)).asnumpy() + inputs = inputs.reshape([3, 1, 4, 5]).repeat(6, axis=1) + time_distributed = TestTimeDistributed(flatten, time_axis=1) + output = time_distributed(Tensor(inputs, mindspore.float32)).asnumpy() + for i in range(output.shape[1]): + assert np.all(output[:, i, :] == output_expect) + print("Flatten op with no reshape axis wrapped successful") diff --git a/tests/st/ops/gpu/test_time_distributed_op.py b/tests/st/ops/gpu/test_time_distributed_op.py new file mode 100644 index 00000000000..6ff85747b2a --- /dev/null +++ b/tests/st/ops/gpu/test_time_distributed_op.py @@ -0,0 +1,198 @@ +# Copyright 2020 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +import numpy as np +import pytest + +import mindspore +import mindspore.context as context +import mindspore.nn as nn +import mindspore.ops as ops +from mindspore import Tensor + +context.set_context(mode=context.GRAPH_MODE, device_target='GPU') + + +class TestTimeDistributed(nn.Cell): + def __init__(self, cell, time_axis, reshape_with_axis=None): + super(TestTimeDistributed, self).__init__() + self.time_distributed = nn.TimeDistributed(cell, time_axis, reshape_with_axis) + + def construct(self, inputs): + return self.time_distributed(inputs) + + +@pytest.mark.level0 +@pytest.mark.platform_x86_gpu_training +@pytest.mark.env_onecard +def test_time_distributed_conv2d(): + inputs = np.random.randint(0, 10, [32, 12, 10, 10]) + conv2d = nn.Conv2d(12, 24, 4, has_bias=False, weight_init='normal') + output_expect = conv2d(Tensor(inputs, mindspore.float32)).asnumpy() + inputs = inputs.reshape([32, 1, 12, 10, 10]).repeat(6, axis=1) + time_distributed = TestTimeDistributed(conv2d, time_axis=1, reshape_with_axis=0) + output = time_distributed(Tensor(inputs, mindspore.float32)).asnumpy() + for i in range(output.shape[1]): + assert np.all(np.abs(output[:, i, :] - output_expect) < 1e-5) + print("Conv2D layer wrapped successful") + + +@pytest.mark.level0 +@pytest.mark.platform_x86_gpu_training +@pytest.mark.env_onecard +def test_time_distributed_maxpool2d(): + inputs = np.random.randint(0, 10, [32, 12, 10, 10]) + pool = nn.MaxPool2d(kernel_size=3, stride=1) + output_expect = pool(Tensor(inputs, mindspore.float32)).asnumpy() + inputs = inputs.reshape([32, 1, 12, 10, 10]).repeat(6, axis=1) + time_distributed = TestTimeDistributed(pool, time_axis=1, reshape_with_axis=0) + output = time_distributed(Tensor(inputs, mindspore.float32)).asnumpy() + for i in range(output.shape[1]): + assert np.all(output[:, i, :] == output_expect) + print("MaxPooling2D layer wrapped successful") + + +@pytest.mark.level0 +@pytest.mark.platform_x86_gpu_training +@pytest.mark.env_onecard +def test_time_distributed_dense(): + inputs = np.random.randint(0, 10, [32, 10]) + dense = nn.Dense(10, 6) + output_expect = dense(Tensor(inputs, mindspore.float32)).asnumpy() + inputs = inputs.reshape([32, 1, 10]).repeat(6, axis=1) + time_distributed = TestTimeDistributed(dense, time_axis=1, reshape_with_axis=0) + output = time_distributed(Tensor(inputs, mindspore.float32)).asnumpy() + for i in range(output.shape[1]): + assert np.all(output[:, i, :] == output_expect) + print("Dense layer wrapped successful") + + +@pytest.mark.level0 +@pytest.mark.platform_x86_gpu_training +@pytest.mark.env_onecard +def test_time_distributed_dense_with_reshape_axis_not_first(): + inputs = np.random.randint(0, 10, [32, 10]) + dense = nn.Dense(10, 6) + output_expect = dense(Tensor(inputs, mindspore.float32)).asnumpy() + inputs = inputs.reshape([1, 32, 10]).repeat(6, axis=0) + time_distributed = TestTimeDistributed(dense, time_axis=0, reshape_with_axis=1) + output = time_distributed(Tensor(inputs, mindspore.float32)).asnumpy() + for i in range(output.shape[0]): + assert np.all(output[i, :] == output_expect) + print("Dense layer wrapped successful") + + +@pytest.mark.level0 +@pytest.mark.platform_x86_gpu_training +@pytest.mark.env_onecard +def test_time_distributed_argmax(): + inputs = np.random.randint(0, 10, [3, 4]) + argmax = ops.Argmax(output_type=mindspore.int32, axis=1) + output_expect = argmax(Tensor(inputs, mindspore.float32)).asnumpy() + inputs = inputs.reshape([3, 1, 4]).repeat(6, axis=1) + time_distributed = TestTimeDistributed(argmax, time_axis=1, reshape_with_axis=0) + output = time_distributed(Tensor(inputs, mindspore.float32)).asnumpy() + for i in range(output.shape[1]): + assert np.all(output[:, i] == output_expect) + print("Argmax op wrapped successful") + + +@pytest.mark.level0 +@pytest.mark.platform_x86_gpu_training +@pytest.mark.env_onecard +def test_time_distributed_flatten(): + inputs = np.random.randint(0, 10, [3, 4, 5]) + flatten = nn.Flatten() + output_expect = flatten(Tensor(inputs, mindspore.float32)).asnumpy() + inputs = inputs.reshape([3, 1, 4, 5]).repeat(6, axis=1) + time_distributed = TestTimeDistributed(flatten, time_axis=1, reshape_with_axis=0) + output = time_distributed(Tensor(inputs, mindspore.float32)).asnumpy() + for i in range(output.shape[1]): + assert np.all(output[:, i, :] == output_expect) + print("Flatten op wrapped successful") + + +@pytest.mark.level0 +@pytest.mark.platform_x86_gpu_training +@pytest.mark.env_onecard +def test_time_distributed_conv2d_no_reshape_axis(): + inputs = np.random.randint(0, 10, [32, 12, 10, 10]) + conv2d = nn.Conv2d(12, 24, 4, has_bias=False, weight_init='normal') + output_expect = conv2d(Tensor(inputs, mindspore.float32)).asnumpy() + inputs = inputs.reshape([32, 1, 12, 10, 10]).repeat(6, axis=1) + time_distributed = TestTimeDistributed(conv2d, time_axis=1) + output = time_distributed(Tensor(inputs, mindspore.float32)).asnumpy() + for i in range(output.shape[1]): + assert np.all(output[:, i, :] == output_expect) + print("Conv2D layer with no reshape axis wrapped successful") + + +@pytest.mark.level0 +@pytest.mark.platform_x86_gpu_training +@pytest.mark.env_onecard +def test_time_distributed_maxpool2d_no_reshape_axis(): + inputs = np.random.randint(0, 10, [32, 12, 10, 10]) + pool = nn.MaxPool2d(kernel_size=3, stride=1) + output_expect = pool(Tensor(inputs, mindspore.float32)).asnumpy() + inputs = inputs.reshape([32, 1, 12, 10, 10]).repeat(6, axis=1) + time_distributed = TestTimeDistributed(pool, time_axis=1) + output = time_distributed(Tensor(inputs, mindspore.float32)).asnumpy() + for i in range(output.shape[1]): + assert np.all(output[:, i, :] == output_expect) + print("MaxPooling2D layer with no reshape axis wrapped successful") + + +@pytest.mark.level0 +@pytest.mark.platform_x86_gpu_training +@pytest.mark.env_onecard +def test_time_distributed_dense_no_reshape_axis(): + inputs = np.random.randint(0, 10, [32, 10]) + dense = nn.Dense(10, 6) + output_expect = dense(Tensor(inputs, mindspore.float32)).asnumpy() + inputs = inputs.reshape([32, 1, 10]).repeat(6, axis=1) + time_distributed = TestTimeDistributed(dense, time_axis=1) + output = time_distributed(Tensor(inputs, mindspore.float32)).asnumpy() + for i in range(output.shape[1]): + assert np.all(output[:, i, :] == output_expect) + print("Dense layer with no reshape axis wrapped successful") + + +@pytest.mark.level0 +@pytest.mark.platform_x86_gpu_training +@pytest.mark.env_onecard +def test_time_distributed_argmax_no_reshape_axis(): + inputs = np.random.randint(0, 10, [3, 4]) + argmax = ops.Argmax(output_type=mindspore.int32, axis=1) + output_expect = argmax(Tensor(inputs, mindspore.float32)).asnumpy() + inputs = inputs.reshape([3, 1, 4]).repeat(6, axis=1) + time_distributed = TestTimeDistributed(argmax, time_axis=1) + output = time_distributed(Tensor(inputs, mindspore.float32)).asnumpy() + for i in range(output.shape[1]): + assert np.all(output[:, i] == output_expect) + print("Argmax op with no reshape axis wrapped successful") + + +@pytest.mark.level0 +@pytest.mark.platform_x86_gpu_training +@pytest.mark.env_onecard +def test_time_distributed_flatten_no_reshape_axis(): + inputs = np.random.randint(0, 10, [3, 4, 5]) + flatten = nn.Flatten() + output_expect = flatten(Tensor(inputs, mindspore.float32)).asnumpy() + inputs = inputs.reshape([3, 1, 4, 5]).repeat(6, axis=1) + time_distributed = TestTimeDistributed(flatten, time_axis=1) + output = time_distributed(Tensor(inputs, mindspore.float32)).asnumpy() + for i in range(output.shape[1]): + assert np.all(output[:, i, :] == output_expect) + print("Flatten op with no reshape axis wrapped successful")